Pulsed Neural Networks for Image Processing

V. Paukstaitis, A. Dosinas. Pulsed Neural Networks for Image Processing // Electronics and Electrical Engineering. - Kaunas: Technologija, 2009. - No. 7(95). - P. 15-20. Ehe information streams model of the artificial pulsed neural network to reserch the performance peculiarities in the up-coming physical systems in the vision applications (sic). The information is transmitted between layers of neurons, performing the convolution, by sequences consisting of pulses of the identical amplitude and width. The simplified theoretical model is tested by the system capable to change the inner and outer performance parameters of the pulsed neuron. The presented model of pulsed network tends to output optimal count of pulses due to negative feedback, which introduces the swinging character of the error function. The paper also presents the quantitative evaluation obtained near minimal error when the pulsed neuron has the response linear and exponential characteristics. The work of both neurons are compared by the extracted factors of the polynomial sum. Therefore, the images were convolved with artificial neural network consisting of the optimized pulse neurons. Ill. 8, bibl. 4 (in English; summaries in English, Russian and Lithuanian).